Body Segment Classification for Visible Human Cross Section Slices.

Xue Z, Antani SK, Long LR, Demner-Fushman D, Thoma GR

IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS), New York, NY, May 2014: pp. 199-204

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Abstract:

Visible human data has been widely used in various medical research and computer science applications. We present a new application for this data: a method to classify which body segment a transverse cross section image belongs to. The labeling of the data is created with the guidance of an online body cross section tutorial. The visual properties of the images are represented using a variety of feature descriptors. To avoid problems that arise from the large dimensionality of features, feature selection is applied. The multi-class SVM is employed as the classifier. Both the CT scans and the color photographs of cryosections of the whole body (male and female) are used to test the proposed method. The high performance with overall accuracy above 98% on both the 2160 CT dataset and the 1870 cryosectional photos show the method is very promising. Because of its observed effectiveness on visible human data, we will extend our approach to classify figures in biomedical articles.

Xue Z, Antani SK, Long LR, Demner-Fushman D, Thoma GR. Body Segment Classification for Visible Human Cross Section Slices. 
IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS), New York, NY, May 2014: pp. 199-204

PDF | URL: http://dx.doi.org/10.1109/CBMS.2014.55